diff toolboxes/FullBNT-1.0.7/netlab3.3/demglm2.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demglm2.m	Tue Feb 10 15:05:51 2015 +0000
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+%DEMGLM2 Demonstrate simple classification using a generalized linear model.
+%
+%	Description
+%	 The problem consists of a two dimensional input matrix DATA and a
+%	vector of classifications T.  The data is  generated from three
+%	Gaussian clusters, and a generalized linear model with softmax output
+%	is trained using iterative reweighted least squares. A plot of the
+%	data together with regions shaded by the classification given by the
+%	network is generated.
+%
+%	See also
+%	DEMGLM1, GLM, GLMTRAIN
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+
+% Generate data from three classes in 2d
+input_dim = 2;
+
+% Fix seeds for reproducible results
+randn('state', 42);
+rand('state', 42);
+
+ndata = 100;
+% Generate mixture of three Gaussians in two dimensional space
+mix = gmm(2, 3, 'spherical');
+mix.priors = [0.4 0.3 0.3];            % Cluster priors
+mix.centres = [2, 2; 0.0, 0.0; 1, -1];  % Cluster centres
+mix.covars = [0.5 1.0 0.6];
+
+[data, label] = gmmsamp(mix, ndata);
+id = eye(3);
+targets = id(label,:);
+
+% Plot the result
+
+clc
+disp('This demonstration illustrates the use of a generalized linear model')
+disp('to classify data from three classes in a two-dimensional space. We')
+disp('begin by generating and plotting the data.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+
+fh1 = figure;
+plot(data(label==1,1), data(label==1,2), 'bo');
+hold on
+axis([-4 5 -4 5]);
+set(gca, 'Box', 'on')
+plot(data(label==2,1), data(label==2,2), 'rx')
+plot(data(label==3, 1), data(label==3, 2), 'go')
+title('Data')
+
+clc
+disp('Now we fit a model consisting of a softmax function of')
+disp('a linear combination of the input variables.')
+disp(' ')
+disp('The model is trained using the IRLS algorithm for up to 10 iterations')
+disp(' ')
+disp('Press any key to continue.')
+pause
+
+net = glm(input_dim, size(targets, 2), 'softmax');
+options = foptions;
+options(1) = 1;
+options(14) = 10;
+net = glmtrain(net, options, data, targets);
+
+disp(' ')
+disp('We now plot the decision regions given by this model.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+
+x = -4.0:0.2:5.0;
+y = -4.0:0.2:5.0;
+[X, Y] = meshgrid(x,y);
+X = X(:);
+Y = Y(:);
+grid = [X Y];
+Z = glmfwd(net, grid);
+[foo , class] = max(Z');
+class = class';
+colors = ['b.'; 'r.'; 'g.'];
+for i = 1:3
+  thisX = X(class == i);
+  thisY = Y(class == i);
+  h = plot(thisX, thisY, colors(i,:));
+  set(h, 'MarkerSize', 8);
+end
+title('Plot of Decision regions')
+
+hold off
+
+clc
+disp('Note that the boundaries of decision regions are straight lines.')
+disp(' ')
+disp('Press any key to end.')
+pause
+close(fh1);
+clear all; 
+